import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.VonMises().__class__.__name__ == 'Bernoulli':
distribution = ot.Bernoulli(0.7)
elif ot.VonMises().__class__.__name__ == 'Binomial':
distribution = ot.Binomial(5, 0.2)
elif ot.VonMises().__class__.__name__ == 'ComposedDistribution':
copula = ot.IndependentCopula(2)
marginals = [ot.Uniform(1.0, 2.0), ot.Normal(2.0, 3.0)]
distribution = ot.ComposedDistribution(marginals, copula)
elif ot.VonMises().__class__.__name__ == 'CumulativeDistributionNetwork':
coll = [ot.Normal(2), ot.Dirichlet([0.5, 1.0, 1.5])]
distribution = ot.CumulativeDistributionNetwork(
coll, ot.BipartiteGraph([[0, 1], [0, 1]]))
elif ot.VonMises().__class__.__name__ == 'Histogram':
distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
elif ot.VonMises().__class__.__name__ == 'KernelMixture':
kernel = ot.Uniform()
sample = ot.Normal().getSample(5)
bandwith = [1.0]
distribution = ot.KernelMixture(kernel, bandwith, sample)
elif ot.VonMises().__class__.__name__ == 'MaximumDistribution':
coll = [
ot.Uniform(2.5, 3.5),
ot.LogUniform(1.0, 1.2),
ot.Triangular(2.0, 3.0, 4.0)
]
distribution = ot.MaximumDistribution(coll)
elif ot.VonMises().__class__.__name__ == 'Multinomial':
distribution = ot.Multinomial(5, [0.2])
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
ot.TESTPREAMBLE()
ot.RandomGenerator.SetSeed(0)
# Instanciate one distribution object
distribution = ot.VonMises(-0.5, 1.5)
print("Distribution ", distribution)
# Is this distribution elliptical ?
print("Elliptical = ", distribution.isElliptical())
# Is this distribution continuous ?
print("Continuous = ", distribution.isContinuous())
# Test for realization of distribution
oneRealization = distribution.getRealization()
print("oneRealization=", oneRealization)
# Test for sampling
size = 10000
oneSample = distribution.getSample(size)
print("oneSample first=", oneSample[0], " last=", oneSample[size - 1])
print("mean=", oneSample.computeMean())
print("covariance=", oneSample.computeCovariance())
size = 100
for i in range(2):
print(
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if (ot.VonMises().__class__.__name__ == 'ComposedDistribution'):
correlation = ot.CorrelationMatrix(2)
correlation[1, 0] = 0.25
aCopula = ot.NormalCopula(correlation)
marginals = [ot.Normal(1.0, 2.0), ot.Normal(2.0, 3.0)]
distribution = ot.ComposedDistribution(marginals, aCopula)
elif (ot.VonMises().__class__.__name__ == 'CumulativeDistributionNetwork'):
distribution = ot.CumulativeDistributionNetwork(
[ot.Normal(2), ot.Dirichlet([0.5, 1.0, 1.5])],
ot.BipartiteGraph([[0, 1], [0, 1]]))
elif (ot.VonMises().__class__.__name__ == 'Histogram'):
distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
else:
distribution = ot.VonMises()
dimension = distribution.getDimension()
if dimension <= 2:
if distribution.getDimension() == 1:
distribution.setDescription(['$x$'])
pdf_graph = distribution.drawPDF()
cdf_graph = distribution.drawCDF()
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
pdf_axis = fig.add_subplot(121)
cdf_axis = fig.add_subplot(122)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)
View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=False)
else:
distribution.setDescription(['$x_1$', '$x_2$'])
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
ot.TESTPREAMBLE()
distribution = ot.VonMises(0.5, 2.5)
size = 10000
sample = distribution.getSample(size)
factory = ot.VonMisesFactory(False)
estimatedDistribution = factory.build(sample)
print("distribution (raw) =", repr(distribution))
print("Estimated distribution (raw) =", repr(estimatedDistribution))
factory = ot.VonMisesFactory(True)
estimatedDistribution = factory.build(sample)
print("distribution (circular) =", repr(distribution))
print("Estimated distribution (circular) =", repr(estimatedDistribution))
estimatedDistribution = factory.build()
print("Default distribution=", estimatedDistribution)
estimatedDistribution = factory.build(
distribution.getParameter())
print("Distribution from parameters=", estimatedDistribution)
estimatedVonMises = factory.buildAsVonMises(sample)
print("VonMises =", distribution)
print("Estimated vonMises=", estimatedVonMises)
estimatedVonMises = factory.buildAsVonMises()
print("Default vonMises=", estimatedVonMises)
estimatedVonMises = factory.buildAsVonMises(distribution.getParameter())
print("VonMises from parameters=", estimatedVonMises)
